qa1.0.0

"""
Quantumaurora: Advanced Transformer-based Language Model
Version: 1.0.0
Created: 2025
"""

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from transformers import PreTrainedTokenizerFast
from tokenizers import Tokenizer, models, trainers, pre_tokenizers, decoders
import math
from typing import Optional, Dict, List, Tuple
from torch.cuda.amp import autocast, GradScaler
from torch.nn.parallel import DistributedDataParallel
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.utils.checkpoint import checkpoint
import json
import os
from datetime import datetime

class QuantumauroraConfig:
    """Configuration class for Quantumaurora model"""
    def __init__(self, 
                 vocab_size: int = 50000,
                 d_model: int = 512,
                 num_heads: int = 8,
                 num_layers: int = 6,
                 d_ff: int = 2048,
                 dropout: float = 0.1,
                 attention_type: str = "full",
                 use_checkpointing: bool = True,
                 max_sequence_length: int = 2048,
                 model_version: str = "1.0.0"):
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.num_heads = num_heads
        self.num_layers = num_layers
        self.d_ff = d_ff
        self.dropout = dropout
        self.attention_type = attention_type
        self.use_checkpointing = use_checkpointing
        self.max_sequence_length = max_sequence_length
        self.model_version = model_version
        self.model_type = "quantumaurora"
        
    def save(self, path: str):
        """Save configuration to JSON file"""
        config_dict = self.__dict__
        config_dict['timestamp'] = datetime.now().isoformat()
        
        with open(path, 'w') as f:
            json.dump(config_dict, f, indent=2)
    
    @classmethod
    def load(cls, path: str) -> 'QuantumauroraConfig':
        """Load configuration from JSON file"""
        with open(path, 'r') as f:
            config_dict = json.load(f)
        
        # Remove timestamp from loaded config
        if 'timestamp' in config_dict:
            del config_dict['timestamp']
            
        return cls(**config_dict)

class Quantumaurora(nn.Module):
    """
    Quantumaurora: Advanced Transformer-based Language Model
    
    A state-of-the-art language model featuring:
    - Multi-head attention with sparse/local patterns
    - Multiple pre-training objectives
    - Gradient checkpointing
    - Mixed precision training
    - Distributed training support
    """
    
    def __init__(self, config: QuantumauroraConfig):
        super().__init__()
        self.config = config
        
        # Model components
        self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)
        self.positional_encoding = PositionalEncoding(config.d_model)
        
        self.transformer_blocks = nn.ModuleList([
            TransformerBlock(
                config.d_model,
                config.num_heads,
                config.d_ff,
                config.dropout,
                config.attention_type
            ) for _ in range(config.num_layers)
        ])
        
        self.pretraining_objectives = PreTrainingObjectives(
            config.d_model,
            config.vocab_size
        )
        
        self.dropout = nn.Dropout(config.dropout)
        
    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
        x = self.token_embedding(x)
        x = self.positional_encoding(x)
        x = self.dropout(x)
        
        for transformer_block in self.transformer_blocks:
            if self.config.use_checkpointing and self.training:
                x = checkpoint(transformer_block, x, mask)
            else:
                x = transformer_block(x, mask)
        
        return self.pretraining_objectives(x)
    
    def save_pretrained(self, path: str):
        """Save model and configuration"""
        os.makedirs(path, exist_ok=True)
        
        # Save configuration
        config_path = os.path.join(path, 'config.json')
        self.config.save(config_path)
        
        # Save model weights
        model_path = os.path.join(path, 'model.pt')
        torch.save(self.state_dict(), model_path)
        
        # Save tokenizer if available
        if hasattr(self, 'tokenizer'):
            tokenizer_path = os.path.join(path, 'tokenizer.json')
            self.tokenizer.save(tokenizer_path)
    
    @classmethod
    def from_pretrained(cls, path: str) -> 'Quantumaurora':
        """Load pretrained model and configuration"""
        config = QuantumauroraConfig.load(os.path.join(path, 'config.json'))
        model = cls(config)
        
        model_path = os.path.join(path, 'model.pt')
        model.load_state_dict(torch.load(model_path))
        
        # Load tokenizer if available
        tokenizer_path = os.path.join(path, 'tokenizer.json')
        if os.path.exists(tokenizer_path):
            model.tokenizer = PreTrainedTokenizerFast.from_file(tokenizer_path)
        
        return model

class QuantumauroraTrainer:
    """Training manager for Quantumaurora model"""
    
    def __init__(self,
                 model: Quantumaurora,
                 train_dataloader: DataLoader,
                 optimizer: torch.optim.Optimizer,
                 device: str = "cuda",
                 use_mixed_precision: bool = True,
                 distributed: bool = True):
        self.model = model
        self.train_dataloader = train_dataloader
        self.optimizer = optimizer
        self.device = device
        self.use_mixed_precision = use_mixed_precision
        self.distributed = distributed
        
        if use_mixed_precision:
            self.scaler = GradScaler()
        
        if distributed:
            self.model = DistributedDataParallel(model)
    
    def train(self, num_epochs: int, save_dir: str = None):
        """Main training loop"""
        best_loss = float('inf')
        
        for epoch in range(num_epochs):
            losses = self.train_epoch(epoch)
            
            # Save checkpoint if this is the best model
            if save_dir and losses['total'] < best_loss:
                best_loss = losses['total']
                self.model.save_pretrained(os.path.join(save_dir, f'checkpoint-{epoch}'))
                
            print(f"Epoch {epoch+1}/{num_epochs}")
            for loss_name, loss_value in losses.items():
                print(f"{loss_name}: {loss_value:.4f}")

def main():
    """Example usage of Quantumaurora"""
    
    # Initialize configuration
    config = QuantumauroraConfig(
        vocab_size=50000,
        d_model=768,
        num_heads=12,
        num_layers=12,
        attention_type="sparse"
    )
    
    # Initialize model
    model = Quantumaurora(config)
    
    # Multi-GPU training if available
    world_size = torch.cuda.device_count()
    if world_size > 1:
        mp.spawn(
            train_distributed,
            args=(world_size, model, dataset),
            nprocs=world_size,
            join=True
        )
    else:
        # Single GPU training
        trainer = QuantumauroraTrainer(
            model=model,
            train_dataloader=train_dataloader,
            optimizer=torch.optim.Adam(model.parameters()),
            use_mixed_precision=True,
            distributed=False
        )
        
        trainer.train(
            num_epochs=10,
            save_dir='quantumaurora_checkpoints'
        )

if __name__ == "__main__":
    main()